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The impact of heterogeneous uptake of HO2 on aerosol surfaces on radical concentrations and the O3 production regime in Beijing in summertime was investigated. The uptake coefficient of HO2 onto aerosol surfaces, γHO2, was calculated for the AIRPRO campaign in Beijing, in summer 2017, as a function of measured aerosol soluble copper concentration, [Cu2+]eff, aerosol liquid water content, [ALWC], and particulate matter concentration, [PM]. An average γHO2 across the entire campaign of 0.070±0.035 was calculated, with values ranging from 0.002 to 0.15, and found to be significantly lower than the value of γHO2=0.2, commonly used in modelling studies. Using the calculated γHO2 values for the summer AIRPRO campaign, OH, HO2 and RO2 radical concentrations were modelled using a box model incorporating the Master Chemical Mechanism (v3.3.1), with and without the addition of γHO2, and compared to the measured radical concentrations. The rate of destruction analysis showed the dominant HO2 loss pathway to be HO2 + NO for all NO concentrations across the summer Beijing campaign, with HO2 uptake contributing % to the total loss of HO2 on average. This result for Beijing summertime would suggest that under most conditions encountered, HO2 uptake onto aerosol surfaces is not important to consider when investigating increasing O3 production with decreasing [PM] across the North China Plain. At low [NO], however, i.e. ppb, which was often encountered in the afternoons, up to 29 % of modelled HO2 loss was due to HO2 uptake on aerosols when calculated γHO2 was included, even with the much lower γHO2 values compared to γHO2= 0.2, a result which agrees with the aerosol-inhibited O3 regime recently proposed by Ivatt et al. (2022). As such it can be concluded that in cleaner environments, away from polluted urban centres where HO2 loss chemistry is not dominated by NO but where aerosol surface area is high still, changes in PM concentration and hence aerosol surface area could still have a significant effect on both overall HO2 concentration and the O3 production regime. Using modelled radical concentrations, the absolute O3 sensitivity to NOx and volatile organic compounds (VOCs) showed that, on average across the summer AIRPRO campaign, the O3 production regime remained VOC-limited, with the exception of a few days in the afternoon when the NO mixing ratio dropped low enough for the O3 regime to shift towards being NOx-limited. The O3 sensitivity to VOCs, the dominant regime during the summer AIRPRO campaign, was observed to decrease and shift towards a NOx-sensitive regime both when NO mixing ratio decreased and with the addition of aerosol uptake. This suggests that if [NOx] continues to decrease in the future, ozone reduction policies focussing solely on NOx reductions may not be as efficient as expected if [PM] and, hence, HO2 uptake to aerosol surfaces continue to decrease. The addition of aerosol uptake into the model, for both the γHO2 calculated from measured data and when using a fixed value of γHO2=0.2, did not have a significant effect on the overall O3 production regime across the campaign. While not important for this campaign, aerosol uptake could be important for areas of lower NO concentration that are already in a NOx-sensitive regime.

Field observation networks are becoming denser, more diverse, and more mobile, while being required to provide real-time results. The ERC urbisphere program is coordinating multiple field campaigns simultaneously to collect datasets on urban atmospheric and environmental conditions and processes in cities of different sizes. The datasets are used for improving climate and weather models and services, including assessing the impact of cities on the atmosphere (e.g. aerosols, greenhouse gases) as well as the exposure of urban populations in the context of atmospheric extreme events (heat waves, heavy precipitation, air pollution). For model development and evaluation, we are using meshed networks of in-situ observations with ground-based and airborne (remote-)sensing platforms. This contribution describes the urbisphere data management infrastructure and processes required to handle a variety of data streams from primarily novel modular observation systems deployed in complex urban environments.The modular observation systems consist of short-term deployed instrumentation and are separated into three thematic modules. Module A aims at characterizing urban form and function affecting urban climates. Module B quantifies the impact of urban emissions (heat, pollutants, greenhouse gases, etc.) on the urban boundary layer over and downwind of cities. Module C provides data on human exposure at street and indoor-level. The three modules are served with consistent data management, documentation and calibration. Systems deployed in Modules A, B and C include customized automatic weather stations, (Doppler) lidars and ceilometers, scintillometers, balloon radio sounding and spectral camera imaging. Systems are street-light-mounted, located on building roof-tops or indoors as well as on mobile platforms (vehicles, drones). Data ingestion processes are automated, delivering moderate data volumes in real-time to central data infrastructure through mobile phone and IOT connectivity. A meta data system helps keep track of the location and configuration of all deployed components and forms the backbone for conversion of instrument records into location-aware, conventions-aligned and quality-assured F.A.I.R. data products. Furthermore, the data management infrastructure provides services (APIs, Apps, IDEs, etc.) for data inspection and computations by scientists and students involved in the campaigns. Select datasets are integrated in near real-time into other global or local data systems such as, e.g., AERONET, the Phenocam Network, ICOS, or PANAME, for multiple uses.Besides technical aspects and design considerations, we discuss how cooperation and attribution are safeguarded when data are being accessed for immediate academic and citizen data science.

Atmospheric boundary layer (ABL) dynamics severely impact the horizontal transport of pollutants as well as their vertical dilution. Despite this, observations of vertical wind profiles and the ABL height are still rare, particularly in cities. Thanks to recent technological advances compact ground-based remote sensing instruments are now available to monitor the heterogeneous urban atmosphere across dense sensor networks. In urban settings, Doppler wind lidars (DWL) and automatic lidars and ceilometers (ALC) are particularly useful as they operate continuously and automatically with very low maintenance under all weather conditions. Thanks to those novel profiling instruments, high-resolution (time and vertical) wind information as well as aerosol backscatter profiles can be recorded.Based on the RI-URBANS (and ICOS-cities) pilot city of Paris, France, we demonstrate what advanced products can be derived using different detailed algorithms, including vertical profiles of horizontal wind and turbulence, boundary layer heights based on aerosol or turbulence indicators, as well as low-level jet characteristics. In Paris, RI-URBANS is embedded in the PANAME initiative that coordinates the synergy between numerous projects that are investigating the urban atmosphere. Clear measurement standards, careful quality control and advanced processing algorithms are required to ensure harmonised products are obtained from the diverse sensor networks that involve instruments of different models from various manufacturers with respective capabilities and limitations.Using the synergy of the different ABL products obtained in the Paris region, it is investigated how the urban boundary layer interacts with the synoptic scale flow, the underlying topography and the urban surface. A combination of wind direction, atmospheric stability and terrain clearly affect shallow boundary layer heights and the low-level jet characteristics. But also spatial variations across the region are registered during deep convective boundary layer development.

The international ACROSS (Atmospheric ChemistRy Of the Suburban forest) intensive field measurement campaign (supported by the Make Our Planet Great Again initiative) took place from June 13 to July 25 2022 in and around the Ile-de-France during an exceptional hot (more than 3°C above the climatological mean for France) and dry summer season. This work will provide a broad classification of weather conditions that occurred during the campaign, useful for further analysis of observations and simulations. Weather scenarios will be analysed in terms of synoptic weather situations, relating transport patterns (regional advection to the Ile-de-France region, plumes from Paris agglomeration), temperature and relative humidity evolution. During the June-July 2022 period, several distinct weather and pollution patterns occurred, among which: (1) two strong heatwaves, with large photochemical activity promoting secondary pollutants build-up (O3, SOA), (2) advection of relatively clean oceanic air masses, and (3) Saharan dust and intense forest fire events with dust and fire aerosol advection to the Ile-de-France region. The analysis is based on meteorological data from several models (GFS, WRF, ARPEGE, AROME) and observations, pollution forecasts from the French Prev’Air system (http://www2.prevair.org/), and from the AirParif Esmeralda platform (http://www.esmeralda-web.fr/accueil/) that have been used for the airborne campaign support. In addition, dedicated simulations with the CHIMERE model (see companion abstract by Di Antonio et al. for this session), and major pollutant observations by air quality networks, from satellites, or performed within ACROSS will be used to analyse how meteorological conditions and weather patterns impacted the spatial distributions of major primary and secondary chemical species. Especially tracers of anthropogenic and biogenic emissions and photochemical activity (O3, PM2.5, OA, BC, NOx, BVOC) will be analysed. Keywords: Meteorology, pollutant distributions, transport patterns, ACROSS, MOPGA

The urban is at the heart of many disciplinary projects covering very broad scientific areas. Acquired data or simulations are often accessible (when they are) via targeted thematic portals. However, the need for transdisciplinarity has been essential for several years to answer specific scientific questions or societal demands. For this, the crossing of human sciences data, health, air quality, land use, emissions inventories, biodiversity, etc., would allow new innovative studies in connection with the city.PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) developed by AERIS was designed as the first brick of a data portal that can promote the discovery, access, cross-referencing and representation of urban data from various sectors with air quality and urban heat islands as a starting point. The portal and future developments will be discussed in this presentation.

Existing CO2 emissions reported by city inventories usually lag real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support the present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, both fossil fuel and biogenic sources and sinks, over six years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the WRF-Chem transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend by around 2 % per year in annual CO2 emissions over the Paris region. The impact of COVID-19 pandemic led to a 13 %±1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Then, annual emissions increased by 5.2 % from 32.6±2.2 MtCO2 in 2020 to 34.3±2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling, and high-precision observations, our current capacity could deliver near real-time CO2 emission estimates at city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.

The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth’s surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sublayer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe., European Cooperation in Science and Technology (COST) PTDC/CTAMET/29678/2017 European Commission, French National Research Agency (ANR) Region Ile-de-France, Spanish Government, Junta de Andalucia, University of Granada, Programa Operativo FEDER Andalucia 2014-2020, Earth System Excellence Units Program, Marie Sklodowska-Curie Action Cofund 2016 EU project - Athenea3i national Portuguese funds through FCT - Fundacao para a Ciencia e Tecnologia, I.P. CA18235 871115 101036245 ANR-20-CE22-0013 PID2020-120015RB-I00 PID2020.117825GB.C21 P18-RT-3820 P20-00136 A-RNM-430-UGR20 754446 UIDB/04683/2020

The main research goal of this paper is to identify the most likely source regions of pollution events at Beromünster, Switzerland. Trace gas concentration and flux observations are essential to investigate emission source regions of greenhouse gases (GHGs) and other pollutants. Here we present an analysis of observations taken at an altitude of 212 m above ground at one of the few European tall towers located close to Beromünster, Switzerland (2017–2020). The relatively high observation height — especially for flux measurements — results in a large tower footprint, i.e., area sampled by the tower, predestined for a source analysis on a large scale. We identify high observations and analyze subsets of the concentrations and fluxes by atmospheric stability conditions (local vertical temperature gradient and atmospheric boundary layer height) to distinguish shorter from longer transport distances. And we split the data by prevailing wind direction as an additional separation of potential source regions. Furthermore, we perform inter-species correlation analyses and a parameterized Flux Footprint (FFP) estimation for the tower. We find that pollution events at Beromünster tower are not associated with local emission sources, but are transported from further away — most prominently from the Northeast (NE) and Southeast (SE), where Zurich and Luzern are located, respectively. All species concentrations are highly correlated during winter, while in summer only a limited inter-species correlation exists. The parameterized annual mean FFP shows an extent of roughly 50-x-25 km along and perpendicular to the main wind axis, respectively.

Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

Measurements of OH, HO2, complex RO2 (alkene- and aromatic-related RO2) and total RO2 radicals taken during the integrated Study of AIR Pollution PROcesses in Beijing (AIRPRO) campaign in central Beijing in the summer of 2017, alongside observations of OH reactivity, are presented. The concentrations of radicals were elevated, with OH reaching up to 2.8×107moleculecm-3, HO2 peaking at 1×109moleculecm-3 and the total RO2 concentration reaching 5.5×109moleculecm-3. OH reactivity (k(OH)) peaked at 89 s−1 during the night, with a minimum during the afternoon of ≈22s-1 on average. An experimental budget analysis, in which the rates of production and destruction of the radicals are compared, highlighted that although the sources and sinks of OH were balanced under high NO concentrations, the OH sinks exceeded the known sources (by 15 ppbv h−1) under the very low NO conditions (

Air quality and meteorology in urban environments are strongly affected by dynamical and turbulent processes occurring in the atmospheric boundary layer. These are largely driven by the interaction between the surface and the atmosphere, including the exchange of momentum, heat, moisture, and various gases and aerosols. Vertical ventilation, horizontal advection, and atmospheric stratification are key processes.To improve the understanding of the exchange processes in the urban atmosphere and to assess the implications of spatial variations in surface roughness, spatially resolved vertical profiles of the horizontal wind are required. In this work, we are implementing a novel “volume wind processing” approach to retrieve horizontal wind information on a 3D spatial grid from observations of a scanning Doppler wind lidar (Vaisala Windcube 400s). Deployed on the rooftop of a tall building in downtown Paris, France, the Doppler lidar is operated with a series of scan strategies to monitor the vertical and horizontal variations of the mean wind field across the city center.In order to quantify the performance of the volume wind processing, an evaluation measurement campaign was performed combining measurements at the Vaisala measurement site and the SIRTA atmospheric observatory (Paris-Saclay) located 3.5 km from each other. The Windcube 400s, located on the Vaisala site, gathered measurements based on different scan patterns (full or sector (>30°) Plan-position Indicator (PPI)), from which wind profiles were retrieved using the volume wind processing. These retrievals were then compared to vertical wind profiles obtained from a previously validated and calibrated Doppler lidar (WLS70) running in a vertical profiling mode located at SIRTA. The comparison is performed over a 30-days period. We found a mean difference (Volume Wind – Vertical Stare) of -0.69 m/s and a standard deviation of 1.32 m/s for 10-min averaged profiles.The ongoing work consists of identifying the sources of uncertainty in the volume wind processing and improving the quality of the retrievals by improving quality control procedures. High-quality wind profile products will then be available for research on the spatial variability of the wind speed profiles, in order to determine the influence of the surface roughness on exchange processes in the Paris urban atmosphere.

The atmospheric boundary layer (ABL) is the layer closest to the Earth’s surface within which most human activities take place. The vertical profile of atmospheric thermodynamic parameters in the ABL impact weather, air quality and climate. However, surface sensor networks and satellite observations do not provide sufficient information on the high temporal variability and strong vertical gradients that occur in the ABL. Thus, the ABL represents an important but rather under-sampled part of the atmosphere. This observational gap currently hampers progress in numerical weather prediction, air quality forecasting and climate assessment. Due to recent technological and methodological advances, ground-based remote sensing instruments are now able to provide high-quality profiles of ABL parameters such as temperature, humidity, wind, aerosol and cloud properties. However, even though state-of-the-art ABL profilers are deployed at numerous sites in Europe, efficient science and technology networking and coordination is still required to exploit this rich dataset effectively. The current lack of data and procedure harmonization often diminishes the potential societal benefits of the existing ABL profiling data. This paper introduces PROBE, a new initiative funded by the European Cooperation in Science and Technology (COST), that aims to broaden the bridge between a wide range of user needs and the science and technology expertise residing in industry and academia, while strengthening and harmonizing methods and procedures to yield higher quality ABL observational data. Here, the challenges, objectives and implementation plan for PROBE are described, highlighting some preliminary results that will be further developed into operational applications during the 4-year duration (2019–2023) of this collaborative project.

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for ABL layers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Initial evaluation of the advanced algorithms STRATfinder (for application to high signal-to-noise ratio (SNR) ALC observations) and CABAM (low-SNR measurements) to automatically derive ABL heights indicates promising performances (Kotthaus et al. 2020).In the framework of the ABL testbed project (funded by ICOS, ACTRIS and E-PROFILE) the two algorithms are now being assessed for application in an operational network setting, such as EUMETNET E-PROFILE. A subset of 11 E-PROFILE sites in a range of geographical and land cover settings across Europe are selected where data from low-SNR and/or high-SNR ALC are available for multiple years. Automatic layer detection is implemented, including instrument-specific corrections and calibrations. Algorithm performance for layer height detection is being evaluated via comparison of results from different ALC and by including reference data from thermodynamic- and turbulence derived layer heights from radiosondes and other ground-based profiling sensors where available. Recommendations are formulated for implementation of automatic ABL height retrievals across a diverse sensor network. A prime example of collaborations within the EU COST action PROBE on profiling the atmospheric boundary layer, the ABL testbed is a crucial step towards harmonised ABL height products at the European scale.ReferencesKotthaus, S, M Haeffelin, MA Drouin, JC Dupont, S Grimmond, A Haefele, M Hervo, Y Poltera, M Wiegner, 2020: Tailored Algorithms for the Detection of the Atmospheric Boundary Layer Height from Common Automatic Lidars and Ceilometers (ALC). Remote Sens, 12, 3259, DOI: 10.3390/rs12193259.

Urban climatology: A cloudy picture for hot cities Compared to nearby rural areas, mega-cities can produce an atmosphere that is, paradoxically, drier yet persistently more cloudy. Cities are known to create an “urban heat island”, but a multi-institution team led by Natalie Theeuwes from the University of Reading now shows that the urban fingerprint extends to an increase in cumulus cloud cover. In an analysis of the Paris and London mega-cities, the team finds that afternoon cloud cover during spring and summer is, on average, several percent higher over the urban core than over surrounding rural areas. The results are initially counter-intuitive, because reduced vegetation in cities also tends to dry the atmosphere, which should reduce cloud cover. By probing detailed ground-based observations in and near London, the researchers explain that buildings release their heat throughout the afternoon, enhancing turbulent mixing and thus the delivery of moisture to clouds. The findings illuminate the widening impact of cities on their environment.

Observations of profiles of winds, aerosol, clouds, winds, temperature and humidity in the lowest few km of the atmosphere from networks of ceilometers, Doppler wind lidars and microwave radiometers are starting to flow in real time to forecasting centers in Europe.\ud \ud To realise the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few km are hardly accessible by satellite, especially in dynamically-active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real-time to forecast centers. The three classes of instruments are: (i) Automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog and volcanic ash, the last two being especially important for air traffic control; (ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind-gusts and low-level jets; and (iii) Microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. Twenty-two European countries and fifteen European National Weather Services are collaborating in the project, that involves the implementation of common operating procedures, instrument calibrations, data formats and retrieval algorithms. Currently, data from 220 ceilometers in 17 countries are being distributed in near real-time to national weather forecast centers; this should soon rise to many hundreds. The wind lidars should start delivering real time data in late 2018, and the plan is to incorporate the microwave radiometers in 2019. Initial data assimilation tests indicate a positive impact of the new data.

A snapshot of web pages describing the site location informationand observations used in the Urban-PLUMBER model evaluation projectfor urban areas. Observational data files themselves are NOT included. Access to observational data files must be separately negotiated with data owners. Webpage available at:https://urban-plumber.github.io/sites

Isoprene is the most important biogenic volatile organic compound in the atmosphere. Its calculated impact on ozone (O3) is critically dependent on the model isoprene oxidation chemical scheme, in particular the way the isoprene-derived nitrates (IN) are treated. By combining gas chromatography with mass spectrometry, we have developed a system capable of separating, and unambiguously measuring, individual IN isomers. In this paper we report measurements from its first field deployment, which took place in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme, along with box model simulations using the Master Chemical Mechanism (MCM) (v.3.3.1) to assess the key processes affecting the production and loss of the IN. Seven individual isoprene nitrates were identified and quantified during the summer campaign: two β-isoprene hydroxy nitrates (IHN); four δ isoprene carbonyl nitrates (ICN); and propanone nitrate. Whilst we had previously demonstrated that the system can measure the four δ-IHN, we found no evidence of them in Beijing. The two β-IHN mixing ratios are well correlated with an R2 value of 0.85. The mean for their ratio ((1-OH, 2-ONO2)-IHN : (4-OH, 3-ONO2)-IHN) is 3.4 and exhibits no clear diel cycle (the numbers in the names indicate the carbon (C) atom in the isoprene chain to which the radical is added). Examining this in a box model demonstrates its sensitivity to nitric oxide (NO), with lower NO mixing ratios favouring (1-OH, 2-ONO2)-IHN over (4-OH, 3-ONO2)-IHN. This is largely a reflection of the modelled ratios of their respective precursor peroxy radicals which, at NO mixing ratios of less than 1 part per billion (ppb), increase substantially with decreasing NO. Interestingly, this ratio in the peroxy radicals still exceeds the kinetic ratio (i.e. their initial ratio based on the yields of the adducts from OH addition to isoprene and the rates of reaction of the adducts with oxygen (O2)) even at NO mixing ratios as high as 100 ppb. The relationship of the observed β-IHN ratio with NO is much weaker than modelled, partly due to far fewer data points, but it agrees with the model simulation in so far as there tend to be larger ratios at sub 1 ppb amounts of NO. Of the δ-ICN, the two trans (E) isomers are observed to have the highest mixing ratios and the mean isomer ratio (E-(4-ONO2, 1-CO)-ICN to E-(1-ONO2, 4-CO)-ICN)) is 1.4, which is considerably lower than the expected ratio of 6 for addition of NO3 in the C1 and C4 carbon positions in the isoprene chain. The MCM produces far more δ-ICN than observed, particularly at night and it also simulates an increase in the daytime δ-ICN that greatly exceeds that seen in the observations. Interestingly, the modelled source of δ-ICN is predominantly during the daytime, due to the presence in Beijing of appreciable daytime amounts of NO3 along with isoprene. The modelled ratios of δ-ICN to propanone nitrate are very different to the observed. This study demonstrates the value of speciated IN measurements to test our understanding of the isoprene degradation chemistry. Our interpretation is limited by the uncertainties in our measurements and relatively small data set, but highlights areas of the isoprene chemistry that warrant further study, in particular the NO3 initiated isoprene degradation chemistry.

An improved version of the near-real time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and -STL modules is evaluated based on 9 years of measurements at the SIRTA observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types, and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 minutes to fog onset, with first high alerts occurring earlier for RAD than STL cases.

The WMO World Weather Research Programme (WWRP) “promotes international and interdisciplinary research for more accurate and reliable forecasts from minutes to seasons, expanding the frontiers of weather science to enhance society’s resilience to high-impact weather and the value of weather information for users. In the 2016-2023 WWRP implementation plan, activities focus on 4 challenges: High-Impact Weather, Water, Urbanization, Evolving technologies. Furthermore, the WMO Global Atmosphere Watch Urban Research Meteorology and Environment (GURME) focus on the development of models and associated research activities to enhance the capabilities in providing urban-environmental forecasting and air quality services, illustrating the linkages between meteorology and air quality (https://public.wmo.int/en/programmes).This talk presents an international Research Demonstration Project (RDP), that will focus on international research on scientific urban issues addressed by both WWRP and GURME. The strategic objective of this RDP is to focus on the Olympic Games of Paris in 2024 in order to advance research on the theme of the “future Meteorological Forecasting systems at 100m (or finer) resolution for urban areas”. Such systems would prefigure the numerical weather prediction at the horizon 2030. The focus will be on themes related to extreme weather events in summer which both are influenced by and impacts urbanization: thunderstorms and strong Urban Heat Islands, and their consequences.There are 5 scientific questions that will be addressed during this Paris RDP:Nowcasting & Numerical Weather Prediction in cities at order 100m resolution High resolution thunderstorm nowcasting (probabilistic and deterministic) in the urban environment, Urban heat islands, cool areas and air quality Nowcasting and forecast in coastal cities (for the Marseilles site) How to improve and better use observational networks in urban areas, including (big) non-conventional data Conception and Communication of tailored weather, climate, environmental information at infra-urban resolution. Several High-Impact weather case studies were selected. Storm cases (starting with one the 10th July 2017) will allow to evaluate the role of the urban area on their enhancement. Extreme Heat wave aggravated by a strong Urban Heat Island are also studied (July 2019). Open urban data describing the agglomerations at very high resolution are provided. New innovative methods to produce maps of urban form characteristics (e.g. from street images) and meteorological data (from personal meteorological stations) will be explored.This talk will describe these scientific questions, as well as the common methodology approach that is being discussed within the partners. A focus will be the international experimental campaign that will take place in 2022 over the Paris agglomeration, with an Intensive Observation Period in the summer 2022. Interactions between urban surface and the atmospheric boundary layer, the interactions between air quality and aerosols between city and biogenic plumes, and the local effect of urban trees on micro-climate and chemistry are some of the axes of the campaign. It will provide additional meteorological and air quality observations, to both help to improve the nowcasting and NWP systems at urban scale, and aim to define the required additional instrumentation that should be deployed during the Olympics games themselves.

Air quality in cities is influenced not only by emissions and chemical transformations but also by the physical state of the atmosphere which varies both temporally and spatially. Increasingly, tall buildings (TB) are common features of the urban landscape, yet their impact on urban air flow and dispersion is not well understood, and their effects are not appropriately captured in parameterisation schemes. Here, hardware models of areas within two global mega-cities (London and Beijing) are used to analyse the impact of TB on flow and transport in isolated and cluster settings. Results show that TB generate strong updrafts and downdrafts that affect street-level flow fields. Velocity differences do not decay monotonically with distance from the TB, especially in the near-wake region where the flow is characterised by recirculating winds and jets. Lateral distance from an isolated TB centreline is crucial, and flow is still strongly impacted at longitudinal distances of several TB heights. Evaluation of a wake-flow scheme (ADMS-Build) in the isolated TB case indicates important characteristics are not captured. There is better agreement for a slender, shorter TB than a taller non-cuboidal TB. Better prediction of flow occurs horizontally further away and vertically further from the surface. TB clusters modify the shape of pollutant plumes. Strong updrafts generated by the overlapping wakes of TB clusters lift pollutants out of the canopy, causing a much deeper tracer plume in the lee of the cluster, and an elevated plume centreline with maximum concentrations around the TB mean height. Enhanced vertical spread of the pollutants in the near-wake of the cluster results in overall lower maximum concentrations, but higher concentrations above the mean TB height. These results have important implications for interpreting observations in areas with TB. Using real world ceilometer observations in two mega-cities (Beijing and Paris), we assess the diurnal seasonal variability of the urban boundary layer and evaluate a mixed layer height (MLH) empirical model with parameters derived from a third mega-city (London). The MLH model works well in central Beijing but less well in suburban Paris. The variability of the physical meteorology across different vertical scales discussed in this paper provides additional context for interpreting air quality observations.

Information on the spatiotemporal characteristics of Beijing's urban-rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (~ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both Local Climate Zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ~ 4.5 °C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

Two urban schemes within the Joint UK Land Environment Simulator\ud (JULES) are evaluated offline against multi-year flux observations in the densely\ud built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab\ud model, used in climate simulations, (ii) the 2-tile canopy model MORUSES (Met\ud Office–Reading Urban Surface Exchange Scheme), used for numerical weather pre-\ud diction over the UK. Offline, both models perform better at the suburban site,\ud where differences between the urban schemes are less pronounced due to larger\ud vegetation fractions. At both sites, the outgoing short- and longwave radiation is\ud more accurately represented than the turbulent heat fluxes. The seasonal varia-\ud tions of model skill are large in London, where the sensible heat flux in autumn and\ud winter is strongly under-predicted if the large city-centre magnitudes of anthro-\ud pogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning.\ud The partitioning of the urban surface into canyon and roof in MORUSES improves\ud this as the roof-tile is modelled with a very low thermal inertia, but phase and\ud amplitude of the gridbox-averaged flux critically depend on accurate knowledge of\ud the plan-area fractions of streets and buildings. Not representing non-urban land-\ud cover (e.g. vegetation, inland water) in London results in severely under-predicted\ud latent heat fluxes. Control runs demonstrate that the skill of both models can be\ud greatly improved by providing accurate land-cover and morphology information\ud and using representative anthropogenic heat emissions, which is essential if the\ud model output is intended to inform integrated urban services.

Measurements of OH, HO2, RO2-complex (alkene and aromatic-related RO2) and total RO2 radicals taken during the AIRPRO campaign in central Beijing in the summer of 2017, alongside observations of OH reactivity are presented. The concentrations of radicals were elevated with OH reaching up to 2.8 × 107 molecule cm−3, HO2 peaked at 1 × 109 molecule cm−3 and the total RO2 concentration reached 5.5 × 109 molecule cm−3. OH reactivity (k(OH)) peaked at 89 s−1 during the night, with a minimum during the afternoons of ~ 22 s−1 on average. An experimental budget analysis, in which the rates of production and destruction of the radicals are compared, highlighted that although the sources and sinks of OH were balanced under high NO concentrations, the OH sinks exceeded the known sources (by 15 ppbv hr−1) under the very low NO conditions (

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

Black carbon (BC) forms an important component of particulate matter globally, due to its impact on climate, the environment, and human health. Identifying and quantifying its emission sources is critical for effective policymaking and achieving the desired reduction in air pollution. In this study, we present the first direct measurements of urban BC fluxes using eddy-covariance. The measurements were made over Beijing within the UK-China Air Pollution and Human Health (APHH) winter 2016 and summer 2017 campaigns. In both seasons, the mean measured BC mass (winter: 5.49 ng m−2 s−1, summer: 6.10 ng m−2 s−1) and number fluxes (winter: 261.25 particles cm−2 s−1, summer: 334.37 particles cm−2 s−1) were similar. Traffic was determined to be the dominant source of the BC fluxes measured during both seasons. The total BC emissions within the 2013 Multi-resolution Emission Inventory for China (MEIC) are on average too high compared to measured fluxes by a factor of 58.8 (winter) and 47.2 (summer). Comparison of MEIC transport sector only are also larger (factor winter: 37.5, summer: 37.7) than the measured flux. Emission ratios of BC/NOx and BC/CO are comparable to vehicular emission control standards implemented in January 2017 for gasoline (China 5) and diesel (China V) engines, indicating reduction of BC emissions within central Beijing and extending this to a larger area would further reduce total BC concentrations.

To capture complex physical processes in cities with high degree of heterogeneity, sophisticated urban land surface models (ULSMs) are used with various anthropogenic activities considered. These ULSMs can be used either offline, using atmospheric measurements as forcing inputs, or online, coupled with large-scale climate models. One downside of using ULSMs in offline mode is that most of atmospheric measurements in cities are spatially limited (e.g. a few points or sites) preventing the physical processes across extremely diverse or heterogeneous conditions in cities from being studied in their entire complexity. Coupling ULSMs with meso-scale models helps us study two-way interactions between the urban surface and atmosphere, and provides spatio-temporal information about the effect of urban climate on various city-related environmental issues such as the urban heat island and urban stormwater.Here we couple and evaluate state-of-the-art surface urban energy and water scheme (SUEWS) with the weather research and forecasting (WRF) model. The coupled system (WRF-SUEWS) is evaluated in two UK cities: London (dense urban) and Swindon (suburban) for four two-week periods in each season. In general, WRF-SUEWS models the surface energy balance fluxes well in both cities across all periods. One strength of the coupled system is the ability to model the spatial and temporal distribution of anthropogenic heat in urban areas. We study how the difference between the anthropogenic heat flux of residential and commercial areas affects the energy balance as well as atmospheric variables over these areas.

International audience; A detailed understanding of atmospheric boundary layer (ABL) processes is key to improve forecasting of pollution dispersion and cloud dynamics in the context of future climate scenarios. International networks of automatic lidars and ceilometers (ALC) are gathering valuable data that allow for the height of the ABL and its sublayers to be derived in near real time. A new generation of advanced methods to automatically detect the ABL heights now exist. However, diversity in ALC models means these algorithms need to be tailored to instrument-specific capabilities. Here, the advanced algorithm STRATfinder is presented for application to high signal-to-noise ratio (SNR) ALC observations, and results are compared to an automatic algorithm designed for low-SNR measurements (CABAM). The two algorithms are evaluated for application in an operational network setting. Results indicate that the ABL heights derived from low-SNR ALC have increased uncertainty during daytime deep convection, while high-SNR observations can have slightly reduced capabilities in detecting shallow nocturnal layers. Agreement between the ALC-based methods is similar when either is compared to the ABL heights derived from temperature profile data. The two independent methods describe very similar average diurnal and seasonal variations. Hence, high-quality products of ABL heights may soon become possible at national and continental scales.

Numerical weather prediction (NWP) of urban aerosols is increasingly sophisticated and accurate. In the absence of large particles (e.g. rain, cloud droplets), information on atmospheric aerosols can be obtained from single wavelength automatic lidars and ceilometers (ALC) that measure profiles of attenuated backscatter (βo). To assess the suitability of ALC profile observations for forecast evaluation and data assimilation, a forward operator is required to convert model variables into the measured quantity. Here, an aerosol forward operator (aerFO) is developed and tested with Met Office NWP data (UKV 1.5 km) to obtain synthetic attenuated backscatter profiles (βm). aerFO requires as input the profiles of bulk aerosol mass mixing ratio and relative humidity to compute βm, plus air temperature and pressure to calculate the effect of water vapour absorption. Bulk aerosol characteristics (e.g. mean radius and number concentration) are used to estimate optical properties. ALC profile observations in London are used to assess βm. A wavelength-dependent extinction enhancement factor accounts for the change in optical properties due to aerosol swelling. Sensitivity studies show the aerFO unattenuated backscatter is very sensitive to the aerosol mass and relative humidity above ∼60–80%. The extinction efficiency is sensitive to the choice of aerosol constituents and to ALC wavelength. Given aerosol is a tracer for boundary layer dynamics, application of the aerFO has proven very useful to evaluate the performance of urban surface parameterisation schemes and their ability to drive growth of the mixing layer. Implications of changing the urban surface scheme within the UKV is explored using two spring cases. For the original scheme, morning βm is too high probably because of delayed vertical mixing. The new scheme reduced this persistence of high morning βm, demonstrating the importance of surface heating processes. Analysis of profiles at five sites on 12 clear-sky days shows a positive, statistically significant relation between the differences of modelled and measured near-surface attenuated backscatter [βm - βo] and near-surface aerosol mass. This suggests errors in near-surface attenuated backscatter can be attributed to errors in the amount of aerosol estimated by the NWP scheme. Correlation increases when cases of high relative humidity in the NWP model are excluded. Given the impact on aerosol optical properties demonstrated, results suggest the use of a fixed, bulk aerosol for urban areas in the UKV should be revisited and the lidar ratio should be constrained. As quality of the observed attenuated backscatter is demonstrated to be critical for performing model evaluation, careful sensor operation and data processing is vital to avoid false conclusions to be drawn about model performance.

In urban areas with high air pollution emissions, the boundary layer volume within which gases and particles are diluted is critical to air quality impacts. With advances in ground-based remote sensing technologies and data processing algorithms, observations of layers forming the atmospheric boundary layer (ABL) are becoming increasingly available at high temporal resolution. Here, mixing height (MH) estimates determined from turbulence measurements of Doppler lidars and aerosol-derived mixed layer height (MLH) based on automatic lidar and ceilometer (ALC) observations within the centre of London are assessed. While MH uncertainty increases with shorter duration of vertical stare sampling within the Doppler lidar scan pattern, instrument-related noise of the ALC may result in large MLH errors due to the challenging task of layer attribution. However, when long time series are assessed most of the algorithm- and instrument-related uncertainties average out and therefore become less critical to overall climatological analyses. Systematic differences occur in nocturnal MH from two nearby (3–4 km) sites but MLH estimates at both sites generally agree with MH obtained at the denser urban setting. During daytime, most spatial variations in ABL structure induced by synoptic conditions or land cover heterogeneity at this scale do not exceed measurement uncertainty. Agreement between MH and MLH is clearly affected by ABL aerosol content and cloud conditions. Discrepancies increase with cloud complexity. On average, MH rises ahead of MLH during the morning growth period and peaks earlier in the day. There is a faster afternoon decay of MLH so that MLH and MH converge again around sunset and often have similar nocturnal values. Results demonstrate that turbulence-derived MH and aerosol-derived MLH should not be used inter-changeably for purposes of model evaluation, interpretation of surface air quality observations or initialisation of chemical transport models.

Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation (M_LW) from the 3D COSMO surface (M_LW^3D), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of M_LW^3D fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of M_LW^3D and DART camera view simulations. At this spatial scale (< 0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by M_LW^3D. By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of M_LW^3D is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising.

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Wood burning is widely used for domestic heating and has been identified as a ubiquitous pollution source in urban areas, especially during cold months. The present study is based on a three and a half winter months field campaign in the Paris region measuring Volatile Organic Compounds (VOCs) by Proton Transfer Reaction Mass Spectrometry (PTR-MS) in addition to Black Carbon (BC). Several VOCs were identified as strongly wood burning-influenced (e.g., acetic acid, furfural), or traffic-influenced (e.g., toluene, C8-aromatics). Methylbutenone, benzenediol and butandione were identified for the first time as wood burning-related in ambient air. A Positive Matrix Factorization (PMF) analysis highlighted that wood burning is the most important source of VOCs during the winter season. (47%). Traffic was found to account for about 22% of the measured VOCs during the same period, whereas solvent use plus background accounted altogether for the remaining fraction. The comparison with the regional emission inventory showed good consistency for benzene and xylenes but revisions of the inventory should be considered for several VOCs such as acetic acid, C9-aromatics and methanol. Finally, complementary measurements acquired simultaneously at other sites in Île-de-France (the Paris region) enabled evaluation of spatial variabilities. The influence of traffic emissions on investigated pollutants displayed a clear negative gradient from roadside to suburban stations, whereas wood burning pollution was found to be fairly homogeneous over the region.

Urban canopy models are essential tools for forecasting weather and air quality in cities. However, they require many surface parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model sensitivity of the single-layer urban canopy model (SLUCM) in the Weather Research and Forecasting (WRF) model to surface parameters in two different configurations, one coupled to the overlying atmosphere (on-line) in a 1D configuration and one without coupling (off-line). A two-day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that the SLUCM reacts differently when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence, in order to fully understand the model sensitivity, atmospheric feedback should be considered.

In this study (Morrison et al., 2021) ground based thermal cameras are used to observe urban surface temperatures (Ts) with an unprecedented combination of: temporal and spatial resolution (5 min and ~ 0.5 m → 2.5 m), spatial extent (3.9 ha), instrument number (6 static cameras) and surface heterogeneity (mixed high rise and vegetation). The camera images are classified by geometry and material properties (surface orientation, albedo, solar irradiance, and shadow history). Unlike previous methods, pixels are objectively classified using sensor view modelling and a detailed three-dimensional surface model (430 m × 430 m extent). From detailed source area analysis, the cameras are shown to observe 9.5% of the study area. Across all camera pixels, the 5th - 95th percentile Ts range is 37.5 K around midday. Roofs Ts has the greatest diurnal range (290.6 K → 329.0 K). Ts differences across sloped roofs with different sun-surface geomeetry reach 23.3 K. Walls of different cardinal orientations consistently differ by >10 K between 10:00 and 15:00. High temporal resolution (5 min) shadow tracking from the classified images is used to model cooling rates, where recently shaded (s. West walls remain warm past sunset and are 1.2 K warmer than north walls at 23:00 (~4 h after sunset). Recently shaded walls cool exponentially to ambient Ts at a similar rate as the ground, but four times slower than roofs. The observaiton methods and observed Ts characteristics are anticipated to have a wide range of applications (e.g. informing future ground-based thermogragy campaign setups, evaluation of urban surface energy balance models, ground-truthing of satellite thermal remote sensing). Morrison, W., Kotthaus, S. and Grimmond, S. (2021) ‘Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability’, Urban Climate, 35, p. 100748. doi: 10.1016/j.uclim.2020.100748.

Numerical weather prediction (NWP) models often parameterise aerosols to reduce computational needs, while aiming to accurately capture their impact adequately. Increasingly, aerosols are monitored in-situ directly and/or indirectly (e.g. by automatic lidars and ceilometers, ALC). ALC measure the aerosol optical characteristic of attenuated backscatter. This can also be estimated using forward models that combine forecast aerosol and relative humidity to parameterise aerosol physical and optical characteristics. The aerFO is one such forward model, designed to use Met Office NWP model output and parameterisations from the MURK visibility scheme. Given the aerFO-MURK scheme link, assessing the aerFO and its output could therefore be used to inform future developments of the MURK scheme. To identify which parameterised physical and optical aerosol characteristics in the scheme are the most critical in urban settings, aerFO is driven with different in-situ aerosol observations at a background site in central London. Estimated attenuated backscatter is then assessed against ALC observations. It is shown that the original MURK scheme parameterisation underestimates the variance of both dry mean volume radius and total number concentration. Representing both the accumulation and coarse mode aerosols in the aerFO reduces the median bias error of estimated attenuated backscatter by 69.1 %. Providing more realistic temporal (monthly to hourly) variability of relative mass for different species leads to little improvement, compared to using monthly climatological means. Numerical experiments show that having more realistic estimates of number concentration is more important than providing more accurate values of the dry mean volume radius for the accumulation mode. Hence, improving the parameterisations for number concentration should be a main focus for further development of the MURK scheme. To estimate aerosol attenuated backscatter, the aerFO requires an extinction to backscatter ratio (i.e. the lidar ratio). In addition to forward modelling, the lidar ratio can also be used with ALC attenuated backscatter to calculate aerosol properties estimated in aerosol forecasts. Here, a model is developed that estimates the ratio using in-situ observations of the number size distribution and speciated aerosol masses. The values of lidar ratio derived at the London background site (14 – 80 sr across selected common lidar wavelengths) compare well to the literature. However, the modelled lidar ratio is unexpectedly correlated to relative humidity. Further, a stronger dependence exists at shorter wavelengths (355 and 532 nm) compared to longer wavelengths (905 and 1064 nm), and is due to the critical relation of lidar wavelength to aerosol size.\ud \ud Keywords: urban aerosols; lidar forward operator; automatic lidar and ceilometers; urban observation network; lidar ratio